Overview

Dataset statistics

Number of variables20
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory160.0 B

Variable types

Categorical6
Numeric14

Alerts

Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_BuyHigh correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioHigh correlation
Avg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_AmtHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
Card_Category is highly imbalanced (79.2%)Imbalance
Dependent_count has 904 (8.9%) zerosZeros
Contacts_Count_12_mon has 399 (3.9%) zerosZeros
Total_Revolving_Bal has 2470 (24.4%) zerosZeros
Avg_Utilization_Ratio has 2470 (24.4%) zerosZeros

Reproduction

Analysis started2023-04-09 14:23:15.105199
Analysis finished2023-04-09 14:24:03.538718
Duration48.43 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Existing Customer
8500 
Attrited Customer
1627 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters172159
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExisting Customer
2nd rowExisting Customer
3rd rowExisting Customer
4th rowExisting Customer
5th rowExisting Customer

Common Values

ValueCountFrequency (%)
Existing Customer 8500
83.9%
Attrited Customer 1627
 
16.1%

Length

2023-04-09T11:24:03.807644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T11:24:03.961221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
customer 10127
50.0%
existing 8500
42.0%
attrited 1627
 
8.0%

Most occurring characters

ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141778
82.4%
Uppercase Letter 20254
 
11.8%
Space Separator 10127
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 23508
16.6%
i 18627
13.1%
s 18627
13.1%
e 11754
8.3%
r 11754
8.3%
u 10127
7.1%
o 10127
7.1%
m 10127
7.1%
x 8500
 
6.0%
n 8500
 
6.0%
Other values (2) 10127
7.1%
Uppercase Letter
ValueCountFrequency (%)
C 10127
50.0%
E 8500
42.0%
A 1627
 
8.0%
Space Separator
ValueCountFrequency (%)
10127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162032
94.1%
Common 10127
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 23508
14.5%
i 18627
11.5%
s 18627
11.5%
e 11754
7.3%
r 11754
7.3%
C 10127
 
6.2%
u 10127
 
6.2%
o 10127
 
6.2%
m 10127
 
6.2%
E 8500
 
5.2%
Other values (5) 28754
17.7%
Common
ValueCountFrequency (%)
10127
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Customer_Age
Real number (ℝ)

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.32596
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:04.088394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814
Coefficient of variation (CV)0.1730523
Kurtosis-0.28861992
Mean46.32596
Median Absolute Deviation (MAD)6
Skewness-0.033605016
Sum469143
Variance64.269307
MonotonicityNot monotonic
2023-04-09T11:24:04.250408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 500
 
4.9%
49 495
 
4.9%
46 490
 
4.8%
45 486
 
4.8%
47 479
 
4.7%
43 473
 
4.7%
48 472
 
4.7%
50 452
 
4.5%
42 426
 
4.2%
51 398
 
3.9%
Other values (35) 5456
53.9%
ValueCountFrequency (%)
26 78
0.8%
27 32
 
0.3%
28 29
 
0.3%
29 56
 
0.6%
30 70
 
0.7%
31 91
0.9%
32 106
1.0%
33 127
1.3%
34 146
1.4%
35 184
1.8%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 101
1.0%
64 43
0.4%
63 65
0.6%
62 93
0.9%
61 93
0.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Length

2023-04-09T11:24:04.412524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T11:24:04.552191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
f 5358
52.9%
m 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10127
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 10127
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Dependent_count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:04.655515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2023-04-09T11:24:04.776922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%

Education_Level
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Graduate
3128 
High School
2013 
Unknown
1519 
Uneducated
1487 
College
1013 
Other values (2)
967 

Length

Max length13
Median length11
Mean length8.9392713
Min length7

Characters and Unicode

Total characters90528
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowGraduate
4th rowHigh School
5th rowUneducated

Common Values

ValueCountFrequency (%)
Graduate 3128
30.9%
High School 2013
19.9%
Unknown 1519
15.0%
Uneducated 1487
14.7%
College 1013
 
10.0%
Post-Graduate 516
 
5.1%
Doctorate 451
 
4.5%

Length

2023-04-09T11:24:04.928477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T11:24:05.120636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
graduate 3128
25.8%
high 2013
16.6%
school 2013
16.6%
unknown 1519
12.5%
uneducated 1487
12.2%
college 1013
 
8.3%
post-graduate 516
 
4.3%
doctorate 451
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75343
83.2%
Uppercase Letter 12656
 
14.0%
Space Separator 2013
 
2.2%
Dash Punctuation 516
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9226
12.2%
e 9095
12.1%
o 7976
10.6%
d 6618
8.8%
t 6549
8.7%
n 6044
8.0%
u 5131
6.8%
r 4095
 
5.4%
l 4039
 
5.4%
h 4026
 
5.3%
Other values (6) 12544
16.6%
Uppercase Letter
ValueCountFrequency (%)
G 3644
28.8%
U 3006
23.8%
S 2013
15.9%
H 2013
15.9%
C 1013
 
8.0%
P 516
 
4.1%
D 451
 
3.6%
Space Separator
ValueCountFrequency (%)
2013
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87999
97.2%
Common 2529
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9226
 
10.5%
e 9095
 
10.3%
o 7976
 
9.1%
d 6618
 
7.5%
t 6549
 
7.4%
n 6044
 
6.9%
u 5131
 
5.8%
r 4095
 
4.7%
l 4039
 
4.6%
h 4026
 
4.6%
Other values (13) 25200
28.6%
Common
ValueCountFrequency (%)
2013
79.6%
- 516
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%

Marital_Status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Married
4687 
Single
3943 
Unknown
749 
Divorced
748 

Length

Max length8
Median length7
Mean length6.6845068
Min length6

Characters and Unicode

Total characters67694
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowUnknown
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 4687
46.3%
Single 3943
38.9%
Unknown 749
 
7.4%
Divorced 748
 
7.4%

Length

2023-04-09T11:24:05.323100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T11:24:05.495252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
married 4687
46.3%
single 3943
38.9%
unknown 749
 
7.4%
divorced 748
 
7.4%

Most occurring characters

ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57567
85.0%
Uppercase Letter 10127
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10122
17.6%
i 9378
16.3%
e 9378
16.3%
n 6190
10.8%
d 5435
9.4%
a 4687
8.1%
l 3943
 
6.8%
g 3943
 
6.8%
o 1497
 
2.6%
k 749
 
1.3%
Other values (3) 2245
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
M 4687
46.3%
S 3943
38.9%
U 749
 
7.4%
D 748
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 67694
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Income_Category
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Less than $40K
3561 
$40K - $60K
1790 
$80K - $120K
1535 
$60K - $80K
1402 
Unknown
1112 

Length

Max length14
Median length12
Mean length11.480103
Min length7

Characters and Unicode

Total characters116259
Distinct characters22
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$60K - $80K
2nd rowLess than $40K
3rd row$80K - $120K
4th rowLess than $40K
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
Less than $40K 3561
35.2%
$40K - $60K 1790
17.7%
$80K - $120K 1535
15.2%
$60K - $80K 1402
 
13.8%
Unknown 1112
 
11.0%
$120K + 727
 
7.2%

Length

2023-04-09T11:24:05.657098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T11:24:06.169253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
5454
19.9%
40k 5351
19.5%
less 3561
13.0%
than 3561
13.0%
60k 3192
11.6%
80k 2937
10.7%
120k 2262
8.2%
unknown 1112
 
4.1%

Most occurring characters

ValueCountFrequency (%)
17303
14.9%
K 13742
11.8%
0 13742
11.8%
$ 13742
11.8%
s 7122
 
6.1%
n 6897
 
5.9%
4 5351
 
4.6%
- 4727
 
4.1%
e 3561
 
3.1%
L 3561
 
3.1%
Other values (12) 26511
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31599
27.2%
Decimal Number 29746
25.6%
Uppercase Letter 18415
15.8%
Space Separator 17303
14.9%
Currency Symbol 13742
11.8%
Dash Punctuation 4727
 
4.1%
Math Symbol 727
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 7122
22.5%
n 6897
21.8%
e 3561
11.3%
a 3561
11.3%
h 3561
11.3%
t 3561
11.3%
k 1112
 
3.5%
o 1112
 
3.5%
w 1112
 
3.5%
Decimal Number
ValueCountFrequency (%)
0 13742
46.2%
4 5351
 
18.0%
6 3192
 
10.7%
8 2937
 
9.9%
1 2262
 
7.6%
2 2262
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
K 13742
74.6%
L 3561
 
19.3%
U 1112
 
6.0%
Space Separator
ValueCountFrequency (%)
17303
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 13742
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4727
100.0%
Math Symbol
ValueCountFrequency (%)
+ 727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66245
57.0%
Latin 50014
43.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 13742
27.5%
s 7122
14.2%
n 6897
13.8%
e 3561
 
7.1%
L 3561
 
7.1%
a 3561
 
7.1%
h 3561
 
7.1%
t 3561
 
7.1%
U 1112
 
2.2%
k 1112
 
2.2%
Other values (2) 2224
 
4.4%
Common
ValueCountFrequency (%)
17303
26.1%
0 13742
20.7%
$ 13742
20.7%
4 5351
 
8.1%
- 4727
 
7.1%
6 3192
 
4.8%
8 2937
 
4.4%
1 2262
 
3.4%
2 2262
 
3.4%
+ 727
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17303
14.9%
K 13742
11.8%
0 13742
11.8%
$ 13742
11.8%
s 7122
 
6.1%
n 6897
 
5.9%
4 5351
 
4.6%
- 4727
 
4.1%
e 3561
 
3.1%
L 3561
 
3.1%
Other values (12) 26511
22.8%

Card_Category
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Blue
9436 
Silver
 
555
Gold
 
116
Platinum
 
20

Length

Max length8
Median length4
Mean length4.1175077
Min length4

Characters and Unicode

Total characters41698
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 9436
93.2%
Silver 555
 
5.5%
Gold 116
 
1.1%
Platinum 20
 
0.2%

Length

2023-04-09T11:24:06.450668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T11:24:06.622522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
blue 9436
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31571
75.7%
Uppercase Letter 10127
 
24.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 10127
32.1%
e 9991
31.6%
u 9456
30.0%
i 575
 
1.8%
v 555
 
1.8%
r 555
 
1.8%
o 116
 
0.4%
d 116
 
0.4%
a 20
 
0.1%
t 20
 
0.1%
Other values (2) 40
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 9436
93.2%
S 555
 
5.5%
G 116
 
1.1%
P 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 41698
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Months_on_book
Real number (ℝ)

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:06.776183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2023-04-09T11:24:06.926058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8125802
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:07.059807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5544079
Coefficient of variation (CV)0.40770496
Kurtosis-1.0061305
Mean3.8125802
Median Absolute Deviation (MAD)1
Skewness-0.16245241
Sum38610
Variance2.4161838
MonotonicityNot monotonic
2023-04-09T11:24:07.181313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
2 1243
12.3%
1 910
 
9.0%
ValueCountFrequency (%)
1 910
 
9.0%
2 1243
12.3%
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
ValueCountFrequency (%)
6 1866
18.4%
5 1891
18.7%
4 1912
18.9%
3 2305
22.8%
2 1243
12.3%
1 910
 
9.0%

Months_Inactive_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:07.302715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2023-04-09T11:24:07.414123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%

Contacts_Count_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:07.543574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2023-04-09T11:24:07.665339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

Credit_Limit
Real number (ℝ)

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.9537
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:07.829393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.7767
Coefficient of variation (CV)1.0529223
Kurtosis1.8089893
Mean8631.9537
Median Absolute Deviation (MAD)2593
Skewness1.6667258
Sum87415795
Variance82605861
MonotonicityNot monotonic
2023-04-09T11:24:07.999332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516 508
 
5.0%
1438.3 507
 
5.0%
9959 18
 
0.2%
15987 18
 
0.2%
23981 12
 
0.1%
2490 11
 
0.1%
6224 11
 
0.1%
3735 11
 
0.1%
7469 10
 
0.1%
2069 8
 
0.1%
Other values (6195) 9013
89.0%
ValueCountFrequency (%)
1438.3 507
5.0%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 2
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 2
 
< 0.1%
1451 2
 
< 0.1%
1452 2
 
< 0.1%
ValueCountFrequency (%)
34516 508
5.0%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34140 1
 
< 0.1%
34058 1
 
< 0.1%
34010 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.8141
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:08.183407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.98734
Coefficient of variation (CV)0.70087503
Kurtosis-1.1459918
Mean1162.8141
Median Absolute Deviation (MAD)591
Skewness-0.14883725
Sum11775818
Variance664204.36
MonotonicityNot monotonic
2023-04-09T11:24:08.355378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
2517 508
 
5.0%
1965 12
 
0.1%
1480 12
 
0.1%
1434 11
 
0.1%
1664 11
 
0.1%
1720 11
 
0.1%
1590 10
 
0.1%
1542 10
 
0.1%
1528 10
 
0.1%
Other values (1964) 7062
69.7%
ValueCountFrequency (%)
0 2470
24.4%
132 1
 
< 0.1%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 2
 
< 0.1%
168 2
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
ValueCountFrequency (%)
2517 508
5.0%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 2
 
< 0.1%
2508 2
 
< 0.1%
2507 4
 
< 0.1%
2506 1
 
< 0.1%
2505 3
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ)

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.1396
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:08.535389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.6853
Coefficient of variation (CV)1.2170994
Kurtosis1.7986173
Mean7469.1396
Median Absolute Deviation (MAD)2665
Skewness1.6616965
Sum75639977
Variance82640560
MonotonicityNot monotonic
2023-04-09T11:24:08.707559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 324
 
3.2%
34516 98
 
1.0%
31999 26
 
0.3%
787 8
 
0.1%
701 7
 
0.1%
713 7
 
0.1%
953 7
 
0.1%
463 7
 
0.1%
990 6
 
0.1%
788 6
 
0.1%
Other values (6803) 9631
95.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
34516 98
1.0%
34362 1
 
< 0.1%
34302 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34140 1
 
< 0.1%
34119 1
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75994065
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:09.226079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.21920677
Coefficient of variation (CV)0.28845248
Kurtosis9.9935012
Mean0.75994065
Median Absolute Deviation (MAD)0.114
Skewness1.7320634
Sum7695.919
Variance0.048051608
MonotonicityNot monotonic
2023-04-09T11:24:09.398007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.791 36
 
0.4%
0.712 34
 
0.3%
0.743 34
 
0.3%
0.718 33
 
0.3%
0.735 33
 
0.3%
0.744 32
 
0.3%
0.699 32
 
0.3%
0.722 32
 
0.3%
0.731 31
 
0.3%
0.631 31
 
0.3%
Other values (1148) 9799
96.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.01 1
 
< 0.1%
0.018 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
 
< 0.1%
0.072 1
 
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
ValueCountFrequency (%)
3.397 1
< 0.1%
3.355 1
< 0.1%
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.357 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.275 1
< 0.1%
2.271 1
< 0.1%

Total_Trans_Amt
Real number (ℝ)

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.0863
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:09.590396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.1293
Coefficient of variation (CV)0.77135847
Kurtosis3.8940234
Mean4404.0863
Median Absolute Deviation (MAD)1308
Skewness2.0410034
Sum44600182
Variance11540487
MonotonicityNot monotonic
2023-04-09T11:24:09.762711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 11
 
0.1%
4509 11
 
0.1%
4518 10
 
0.1%
2229 10
 
0.1%
4220 9
 
0.1%
4869 9
 
0.1%
4037 9
 
0.1%
4313 9
 
0.1%
4498 9
 
0.1%
4042 9
 
0.1%
Other values (5023) 10031
99.1%
ValueCountFrequency (%)
510 1
< 0.1%
530 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
643 1
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Total_Trans_Ct
Real number (ℝ)

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:09.944819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2023-04-09T11:24:10.116786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
71 203
 
2.0%
75 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
74 190
 
1.9%
78 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71222238
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:10.308580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.23808609
Coefficient of variation (CV)0.33428617
Kurtosis15.689293
Mean0.71222238
Median Absolute Deviation (MAD)0.119
Skewness2.0640306
Sum7212.676
Variance0.056684987
MonotonicityNot monotonic
2023-04-09T11:24:10.478079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 171
 
1.7%
1 166
 
1.6%
0.5 161
 
1.6%
0.75 156
 
1.5%
0.6 113
 
1.1%
0.8 101
 
1.0%
0.714 92
 
0.9%
0.833 85
 
0.8%
0.778 69
 
0.7%
0.625 63
 
0.6%
Other values (820) 8950
88.4%
ValueCountFrequency (%)
0 7
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 3
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3.714 1
 
< 0.1%
3.571 1
 
< 0.1%
3.5 1
 
< 0.1%
3.25 1
 
< 0.1%
3 2
< 0.1%
2.875 1
 
< 0.1%
2.75 1
 
< 0.1%
2.571 1
 
< 0.1%
2.5 3
< 0.1%
2.429 1
 
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27489355
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-04-09T11:24:10.672699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.27569147
Coefficient of variation (CV)1.0029026
Kurtosis-0.79497195
Mean0.27489355
Median Absolute Deviation (MAD)0.176
Skewness0.718008
Sum2783.847
Variance0.076005786
MonotonicityNot monotonic
2023-04-09T11:24:10.852982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
0.073 44
 
0.4%
0.057 33
 
0.3%
0.048 32
 
0.3%
0.06 30
 
0.3%
0.061 29
 
0.3%
0.045 29
 
0.3%
0.059 28
 
0.3%
0.069 28
 
0.3%
0.053 27
 
0.3%
Other values (954) 7377
72.8%
ValueCountFrequency (%)
0 2470
24.4%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.006 3
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 4
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%

Interactions

2023-04-09T11:23:58.795522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:18.823309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:21.334888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:23.822837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:26.369313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:28.906625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:31.495244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:34.100137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:37.015745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:39.592626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:42.459342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:45.787590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:50.111326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:54.625150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:59.033767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:19.051928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:21.500895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:23.978588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:26.535471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:29.078447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:31.667390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:34.275902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:37.187520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:39.776604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:42.667063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:46.092265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:50.586098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:54.884800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:59.312375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:19.223737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:21.672695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:24.138375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:26.707245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:29.254221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:31.847149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:34.483626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:37.359284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:39.958699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:42.870791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:46.390635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:50.837770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:55.160432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:59.550626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:19.383484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:21.832451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:24.288779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:26.871022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:29.421989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:32.010931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:34.655396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:37.519073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:40.138459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:43.074518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:46.702216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:51.081433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:55.417589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:59.797983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:19.553980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:22.008249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:24.458292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:27.054776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:29.609735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:32.190691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:34.847711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:37.692711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:40.324327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:43.298219image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:47.002816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:51.361061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:55.703649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:24:00.083101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:19.729713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:22.183980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:24.626030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:27.234537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:29.797484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:32.378440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:35.047410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:37.875392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:40.524061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:43.513959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:47.272124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:51.624711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:55.999251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:24:00.368213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:19.901528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:22.362064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:24.801828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:27.420647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:29.981279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:32.566200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:35.239154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:38.059146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:40.715903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:43.785943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:47.575719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:51.892382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:56.290863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:24:00.637694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:20.083897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:22.545819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:24.981593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:27.612393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:30.176978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:32.761927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:35.438926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:38.250891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:40.920615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:44.058433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:47.903280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:52.171975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:56.582474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:24:00.885051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:20.259712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:22.717910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:25.141341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:27.788119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:30.356739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:32.945715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:35.625299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:38.428833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:41.108679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:44.284828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:48.128381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:52.739216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:56.949980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2023-04-09T11:23:25.321101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:27.975868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2023-04-09T11:23:31.123742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:33.724674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:36.623070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:39.224201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:41.999956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:45.296247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:49.247511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:54.073887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:58.224277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:24:02.319718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:21.164747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:23.643041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:26.201223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:28.730862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:31.315521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:33.920377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:36.824003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:39.416861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:42.227657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:45.535964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:49.619381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:54.361503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-09T11:23:58.510379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2023-04-09T11:24:11.036869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Customer_AgeDependent_countMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_FlagGenderEducation_LevelMarital_StatusIncome_CategoryCard_Category
Customer_Age1.000-0.1440.769-0.0140.044-0.0140.0020.014-0.002-0.071-0.039-0.054-0.0400.0110.0240.0000.0140.0820.0840.021
Dependent_count-0.1441.000-0.115-0.036-0.009-0.0410.051-0.0040.054-0.0260.0580.0530.009-0.0350.0210.0000.0010.0370.0430.018
Months_on_book0.769-0.1151.000-0.0140.057-0.0080.0070.0060.008-0.054-0.029-0.039-0.034-0.0040.0190.0110.0020.0430.0460.013
Total_Relationship_Count-0.014-0.036-0.0141.000-0.0070.061-0.0590.012-0.0710.026-0.279-0.2270.0240.0650.1660.0000.0000.0220.0070.067
Months_Inactive_12_mon0.044-0.0090.057-0.0071.0000.030-0.028-0.043-0.016-0.019-0.032-0.051-0.047-0.0270.1960.0190.0000.0070.0170.000
Contacts_Count_12_mon-0.014-0.041-0.0080.0610.0301.0000.023-0.0450.033-0.021-0.167-0.168-0.093-0.0590.2390.0590.0000.0070.0150.010
Credit_Limit0.0020.0510.007-0.059-0.0280.0231.0000.1310.9310.0210.0280.034-0.011-0.4170.0320.4390.0000.0260.2780.335
Total_Revolving_Bal0.014-0.0040.0060.012-0.043-0.0450.1311.000-0.1540.0360.0180.0400.0780.7090.4020.0330.0070.0120.0220.019
Avg_Open_To_Buy-0.0020.0540.008-0.071-0.0160.0330.931-0.1541.0000.0070.0220.022-0.040-0.6860.0190.4400.0000.0280.2780.337
Total_Amt_Chng_Q4_Q1-0.071-0.026-0.0540.026-0.019-0.0210.0210.0360.0071.0000.1350.0850.3020.0330.1840.0440.0000.0530.0150.024
Total_Trans_Amt-0.0390.058-0.029-0.279-0.032-0.1670.0280.0180.0220.1351.0000.8800.2230.0190.3250.2470.0120.1040.0930.154
Total_Trans_Ct-0.0540.053-0.039-0.227-0.051-0.1680.0340.0400.0220.0850.8801.0000.2330.0400.4580.1630.0040.0990.0560.109
Total_Ct_Chng_Q4_Q1-0.0400.009-0.0340.024-0.047-0.093-0.0110.078-0.0400.3020.2230.2331.0000.0940.3140.0500.0000.0300.0230.000
Avg_Utilization_Ratio0.011-0.035-0.0040.065-0.027-0.059-0.4170.709-0.6860.0330.0190.0400.0941.0000.2410.2790.0000.0270.1650.149
Attrition_Flag0.0240.0210.0190.1660.1960.2390.0320.4020.0190.1840.3250.4580.3140.2411.0000.0360.0250.0170.0280.000
Gender0.0000.0000.0110.0000.0190.0590.4390.0330.4400.0440.2470.1630.0500.2790.0361.0000.0110.0090.8390.084
Education_Level0.0140.0010.0020.0000.0000.0000.0000.0070.0000.0000.0120.0040.0000.0000.0250.0111.0000.0110.0170.000
Marital_Status0.0820.0370.0430.0220.0070.0070.0260.0120.0280.0530.1040.0990.0300.0270.0170.0090.0111.0000.0080.028
Income_Category0.0840.0430.0460.0070.0170.0150.2780.0220.2780.0150.0930.0560.0230.1650.0280.8390.0170.0081.0000.053
Card_Category0.0210.0180.0130.0670.0000.0100.3350.0190.3370.0240.1540.1090.0000.1490.0000.0840.0000.0280.0531.000

Missing values

2023-04-09T11:24:02.720704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T11:24:03.259874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Attrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
0Existing Customer45M3High SchoolMarried$60K - $80KBlue3951312691.077711914.01.3351144421.6250.061
1Existing Customer49F5GraduateSingleLess than $40KBlue446128256.08647392.01.5411291333.7140.105
2Existing Customer51M3GraduateMarried$80K - $120KBlue364103418.003418.02.5941887202.3330.000
3Existing Customer40F4High SchoolUnknownLess than $40KBlue343413313.02517796.01.4051171202.3330.760
4Existing Customer40M3UneducatedMarried$60K - $80KBlue215104716.004716.02.175816282.5000.000
5Existing Customer44M2GraduateMarried$40K - $60KBlue363124010.012472763.01.3761088240.8460.311
6Existing Customer51M4UnknownMarried$120K +Gold4661334516.0226432252.01.9751330310.7220.066
7Existing Customer32M0High SchoolUnknown$60K - $80KSilver2722229081.0139627685.02.2041538360.7140.048
8Existing Customer37M3UneducatedSingle$60K - $80KBlue3652022352.0251719835.03.3551350241.1820.113
9Existing Customer48M2GraduateSingle$80K - $120KBlue3663311656.016779979.01.5241441320.8820.144
Attrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
10117Existing Customer57M2GraduateMarried$80K - $120KBlue4063417925.0190916016.00.712174981110.8200.106
10118Attrited Customer50M1UnknownUnknown$80K - $120KBlue366349959.09529007.00.82510310631.1000.096
10119Attrited Customer55F3UneducatedSingleUnknownBlue4743314657.0251712140.00.1666009530.5140.172
10120Existing Customer54M1High SchoolSingle$60K - $80KBlue3452013940.0210911831.00.660155771140.7540.151
10121Existing Customer56F1GraduateSingleLess than $40KBlue504143688.06063082.00.570145961200.7910.164
10122Existing Customer50M2GraduateSingle$40K - $60KBlue403234003.018512152.00.703154761170.8570.462
10123Attrited Customer41M2UnknownDivorced$40K - $60KBlue254234277.021862091.00.8048764690.6830.511
10124Attrited Customer44F1High SchoolMarriedLess than $40KBlue365345409.005409.00.81910291600.8180.000
10125Attrited Customer30M2GraduateUnknown$40K - $60KBlue364335281.005281.00.5358395620.7220.000
10126Attrited Customer43F2GraduateMarriedLess than $40KSilver2562410388.019618427.00.70310294610.6490.189